From 28d4733e77036b64fa712ebd39f080c419675d89 Mon Sep 17 00:00:00 2001 From: Sanyam Bhutani Date: Wed, 15 Jan 2025 10:53:33 -0800 Subject: [PATCH] fix links --- 3p-integrations/llama_on_prem.md | 2 +- end-to-end-use-cases/Multi-Modal-RAG/README.md | 2 +- getting-started/README.md | 2 +- getting-started/finetuning/finetune_vision_model.md | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/3p-integrations/llama_on_prem.md b/3p-integrations/llama_on_prem.md index f9fc877de..fea53cf05 100644 --- a/3p-integrations/llama_on_prem.md +++ b/3p-integrations/llama_on_prem.md @@ -1,6 +1,6 @@ # Llama 3 On-Prem Inference Using vLLM and TGI -Enterprise customers may prefer to deploy Llama 3 on-prem and run Llama in their own servers. This tutorial shows how to use Llama 3 with [vLLM](https://github.com/vllm-project/vllm) and Hugging Face [TGI](https://github.com/huggingface/text-generation-inference), two leading open-source tools to deploy and serve LLMs, and how to create vLLM and TGI hosted Llama 3 instances with [LangChain](https://www.langchain.com/), an open-source LLM app development framework which we used for our other demo apps: [Getting to Know Llama](https://github.com/meta-llama/llama-recipes/blob/main/recipes/quickstart/Getting_to_know_Llama.ipynb), Running Llama 3 [locally](https://github.com/meta-llama/llama-recipes/blob/main/recipes/quickstart/Running_Llama3_Anywhere/Running_Llama_on_Mac_Windows_Linux.ipynb) and [in the cloud](https://github.com/meta-llama/llama-recipes/blob/main/recipes/quickstart/RAG/hello_llama_cloud.ipynb). See [here](https://medium.com/@rohit.k/tgi-vs-vllm-making-informed-choices-for-llm-deployment-37c56d7ff705) for a detailed comparison of vLLM and TGI. +Enterprise customers may prefer to deploy Llama 3 on-prem and run Llama in their own servers. This tutorial shows how to use Llama 3 with [vLLM](https://github.com/vllm-project/vllm) and Hugging Face [TGI](https://github.com/huggingface/text-generation-inference), two leading open-source tools to deploy and serve LLMs, and how to create vLLM and TGI hosted Llama 3 instances with [LangChain](https://www.langchain.com/), an open-source LLM app development framework which we used for our other demo apps: [Getting to Know Llama](../getting-started/build_with_Llama_3_2.ipynb), Running Llama 3 [locally](https://github.com/meta-llama/llama-recipes/blob/main/recipes/quickstart/Running_Llama3_Anywhere/Running_Llama_on_Mac_Windows_Linux.ipynb) and [in the cloud](https://github.com/meta-llama/llama-recipes/blob/main/recipes/quickstart/RAG/hello_llama_cloud.ipynb). See [here](https://medium.com/@rohit.k/tgi-vs-vllm-making-informed-choices-for-llm-deployment-37c56d7ff705) for a detailed comparison of vLLM and TGI. For [Ollama](https://ollama.com) based on-prem inference with Llama 3, see the Running Llama 3 locally notebook above. diff --git a/end-to-end-use-cases/Multi-Modal-RAG/README.md b/end-to-end-use-cases/Multi-Modal-RAG/README.md index 4db178fec..cc4a0dcd5 100644 --- a/end-to-end-use-cases/Multi-Modal-RAG/README.md +++ b/end-to-end-use-cases/Multi-Modal-RAG/README.md @@ -32,7 +32,7 @@ Here's the detailed outline: In this step we start with an unlabeled dataset and use the image captioning capability of the model to write a description of the image and categorize it. -[Notebook for Step 1](./notebooks/Part_1_Data_Preparation.ipynb) and [Script for Step 1](./scripts/label_script.py) +[Notebook for Step 1](./notebooks/Part_1_Data_Preperation.ipynb) and [Script for Step 1](./scripts/label_script.py) To run the script (remember to set n): ``` diff --git a/getting-started/README.md b/getting-started/README.md index bfde987b8..db4f1eec9 100644 --- a/getting-started/README.md +++ b/getting-started/README.md @@ -3,7 +3,7 @@ If you are new to developing with Meta Llama models, this is where you should start. This folder contains introductory-level notebooks across different techniques relating to Meta Llama. * The [Build_with_Llama 3.2](./build_with_Llama_3_2.ipynb) notebook showcases a comprehensive walkthrough of the new capabilities of Llama 3.2 models, including multimodal use cases, function/tool calling, Llama Stack, and Llama on edge. -* The [Prompt_Engineering_with_Llama](./Prompt_Engineering_with_Llama_3.ipynb) notebook showcases the various ways to elicit appropriate outputs from Llama. Take this notebook for a spin to get a feel for how Llama responds to different inputs and generation parameters. +* The [Prompt_Engineering_with_Llama](./Prompt_Engineering_with_Llama.ipynb) notebook showcases the various ways to elicit appropriate outputs from Llama. Take this notebook for a spin to get a feel for how Llama responds to different inputs and generation parameters. * The [inference](./inference/) folder contains scripts to deploy Llama for inference on server and mobile. See also [3p_integrations/vllm](../3p-integrations/vllm/) and [3p_integrations/tgi](../3p-integrations/tgi/) for hosting Llama on open-source model servers. * The [RAG](./RAG/) folder contains a simple Retrieval-Augmented Generation application using Llama. * The [finetuning](./finetuning/) folder contains resources to help you finetune Llama on your custom datasets, for both single- and multi-GPU setups. The scripts use the native llama-recipes finetuning code found in [finetuning.py](../src/llama_recipes/finetuning.py) which supports these features: diff --git a/getting-started/finetuning/finetune_vision_model.md b/getting-started/finetuning/finetune_vision_model.md index d08687968..6d0c1e021 100644 --- a/getting-started/finetuning/finetune_vision_model.md +++ b/getting-started/finetuning/finetune_vision_model.md @@ -28,7 +28,7 @@ For **finetuning with LLM freeze using FSDP**, we can run the following code: For more details about the finetuning configurations, please read the [finetuning readme](./README.md). -For more details about local inference with the fine-tuned checkpoint, please read [Inference with FSDP checkpoints section](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/inference/local_inference#inference-with-fsdp-checkpoints) to learn how to convert the FSDP weights into a consolidated Hugging Face formatted model for local inference. +For more details about local inference with the fine-tuned checkpoint, please read [Inference with FSDP checkpoints section](../../getting-started/inference/local_inference/#inference-with-fsdp-checkpoints) to learn how to convert the FSDP weights into a consolidated Hugging Face formatted model for local inference. ### How to use a custom dataset to fine-tune vision model